Bhanuka Gamachchige for NORVI Controllers

Posted on Feb 10

Building a Multi-Year Battery Life IoT Data Logger: A Deep Dive into Ultra-Low-Power Design

#iot #automation #tutorial

Introduction

Ever wondered how industrial IoT devices achieve multi-year battery life while transmitting data over cellular networks? In this article, I'll walk you through a real-world implementation of an ultra-low-power industrial data logger that monitors diesel fuel levels remotely and achieves an impressive 3-11 year battery life.

We'll be using the EC-M12-BC-C6-C-A, a battery-powered device with an STM32L0 series microcontroller and a SIM7070 LTE modem – a common setup in industrial monitoring applications.

The Challenge

Remote monitoring systems face a critical tradeoff: you need frequent data updates for real-time insights, but each transmission cycle drains your battery. Cellular modems are particularly power-hungry, with peak currents reaching 840mA during network initialisation.

The goal? Design a system that can:

Read analog sensor data accurately
Transmit to cloud dashboard via 4G/LTE-M
Operate for years on primary lithium batteries
Handle harsh industrial environments

System Architecture

[4-20mA Fuel Sensor] → [STM32L0 MCU] → [SIM7070 Modem] → [4G Network] → [ThingsBoard Dashboard]

Hardware Components

MCU: STM32L0
Cellular: SIM7070 NB-IoT/LTE-M modem
Power: 2× ER34615H lithium batteries (19,000mAh each)
Sensor: 4-20mA diesel fuel tank level sensor

The Power Consumption Breakdown

Here's where it gets interesting. The system operates in three distinct stages:

Stage 1: Modem Initialization (60 seconds)

Average Current: 17.71mA
Peak Current: 840mA (during network registration)
Energy: 3.93 J

This is the power-hungry phase. The SIM7070 needs to:

Power up the RF circuitry
Search for cellular networks
Register with the tower
Establish GPRS/LTE connection

Key Design Decision: A 1000µF tantalum capacitor placed near the modem acts as a local energy reservoir, smoothing out the 30-40ms current spikes that can exceed 300mA.

Stage 2: Sensor Read + Data Transmission (7.5 seconds)

Average Current: 11.24mA
Energy: 0.31 J

During this phase:

Read 4-20mA sensor (fuel level)
Read battery voltage
Publish telemetry to ThingsBoard via MQTT
Disable all peripherals

Stage 3: Deep Sleep (15 minutes)

Average Current: 1.05µA
Energy: 0.0035 J

This is where the magic happens. The STM32L0 enters shutdown mode:

All peripherals powered down
Only RTC remains active
Occasional 2-3mA spikes for RTC maintenance (negligible impact)

The Math Behind Battery Life

With 38,000mAh capacity, here's the battery life estimation:

Average Current Formula:

I_avg = (I_sleep × T_sleep + I_active × T_active) / (T_sleep + T_active)

Results:

The 4× increase in sleep time gives you a 3.4× improvement in battery life. This demonstrates the power of duty cycling in embedded systems.

Software Implementation Highlights

Low-Power Mode Configuration

// STM32L0 shutdown mode configuration
#include <STM32LowPower.h>
void setup() {
  Serial.begin(9600);
  LowPower.begin();                   //Initiate low power mode
}
void loop() {
  Serial.println("Entering low power mode...");

  SPI.end();
  delay(1000);
  Wire2.end();
  delay(1000);


  digitalWrite(BOOST_EN, LOW);
  delay(1000);

  // === Fully cut power to GSM ===
  digitalWrite(GSM_POWER, HIGH);  // Depending on your wiring,   HIGH disables modem regulator
  delay(2000);

LowPower.shutdown(900000);
}

Efficient MQTT Publishing

The system uses MQTT over LTE for minimal protocol overhead:

void publishTelemetry() {
 int16_t adc0, adc1, adc2, adc3;

  adc1 = ads1.readADC_SingleEnded(1) * mA_Factor;
  adc2 = ads1.readADC_SingleEnded(2);


  float voltage2 = adc2 * 0.125 / 1000.0 / VOLTAGE_DIVIDER_RATIO;

  Serial.print("AIN1: "); Serial.print(adc0); Serial.println("  ");
  Serial.print("AIN2: "); Serial.print(adc1); Serial.println("  ");
  Serial.print("Voltage: "); Serial.print(voltage2); Serial.println("  ");


  float fuel_percent = (adc1 - 4.0) * 100.0 / 16.0;
  fuel_percent = constrain(fuel_percent, 0, 100);

  // Fuel height
  float fuel_level_mm = (fuel_percent / 100.0) * 1000;

  // Fault detection
  bool fuel_sensor_fault = false;
  if (adc1 < 3.6 || adc1 > 22.0) {
    fuel_sensor_fault = true;
  }

  StaticJsonDocument<200> doc;
  doc["fuel_level_percent"] = fuel_percent;
  doc["fuel_level_mm"]      = fuel_level_mm;
  doc["battery_voltage"]    = voltage2;
  doc["fuel_sensor_fault"]  = fuel_sensor_fault;

  String jsonStr;
  serializeJson(doc, jsonStr);

  mqtt.publish("v1/devices/me/telemetry", jsonStr.c_str());
  Serial.print("Published: ");
  Serial.println(jsonStr);

}

Sensor Fault Detection

The system monitors for open/short circuits in the 4-20mA loop:

< 3.8mA: Sensor fault (open circuit)
> 20.5mA: Sensor fault (short circuit)
4-20mA: Normal operation

Real-World Considerations

Cellular Signal Strength
Poor signal = higher power consumption. The modem will retry connections and transmit at higher power levels, significantly reducing battery life.
Sensor Current Impact
The 4-20mA sensor itself consumes power proportional to the measured value. A full tank (20mA) drains more power than an empty tank (4mA).
Temperature Effects
Lithium batteries lose capacity in extreme cold. Consider temperature-compensated battery life calculations for outdoor installations.
Network Protocol Choice
NB-IoT and LTE-M are specifically designed for IoT applications with lower power consumption than standard LTE. Choose wisely based on your region's network availability.

Lessons Learned

Duty cycling is king: The difference between 15-minute and 1-hour intervals is massive (3.36 vs 11.4 years).
Modem initialization dominates: Even though it's only 1 minute per cycle, it consumes 85% of the total energy per transmission.
Hardware matters: The STM32L0's 1.05µA shutdown current makes multi-year operation possible. A standard MCU would drain batteries in months.

Future Improvements

Implement adaptive transmission intervals based on fuel level change rate
Implement predictive maintenance alerts based on battery voltage trends

Conclusion

Building ultra-low-power IoT devices requires careful consideration of every milliamp-hour. By combining:

Efficient duty cycling
Ultra-low-power MCU sleep modes
Optimized cellular communication
Smart peripheral management

We achieved a system that can monitor remote industrial assets for over 3 years on battery power alone, with the potential to extend that to 11+ years with less frequent updates.

The complete source code and detailed setup instructions are available on GitHub.

Check the product (EC-M12-BC-C6-C-A) documentation.

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